地铁列车碳滑板磨损区域及磨耗预测

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关键词：地铁列车；智慧运维；碳滑板磨耗预测；变化趋势；长短期记忆网络；安全性中图分类号： U264.3+4 文献标志码：B doi：10.20213/j.cnki.tdcl.2025.01.011

Abstract：During the in-depth investigation of the maintenance of urban rail vehicles，it was found that the carbon slider of pantographs had serious wear problems，which had a significant impact on the integration of pantograph and catenary and the safe operation of the train. In order to ensure the normal operation of the new carbon sliders，it is necessary to acurately predict the wear amount and changing trend of carbon sliders. In the context of the rapid development of intelligent operation and maintenance systems，this paper comprehensively considers traditional statistical dataand introduces Long Short-Term Memory network （LSTM）to predict the wear area and wear amount of carbon sliders. By applying different prediction algorithm models for comparative analysis，，the test results show that the accuracy of LSTM network in predicting wear of carbon sliders is about 10% higher than other machine learning models，indicating that LSTM network has significant advantages in predicting the wear of carbon sliders in multiple replacement cycles. Specifically，this paper systematically analyzes and predicts the wear data of carbon sliders by establishing and training LSTM model.The comparison results show that the LSTM model can not only accurately capture the wear trend，but also its predicted data are highly consistent with the actual test results，which basically meets the needs of actual use.It can provide strong technical support for the maintenance and management of urban rail vehicles， effectively improve the operation safety，and prolong the service life of carbon sliders.

Key words： subway train； intelligent operation and maintenance； wear prediction for carbon slider changing trend;LSTM network；safety

在城市轨道交通各个线路的检修车辆段与停车场中，每日需要进行全线所有运营车辆的日常检查，通过日检和计划修对车辆易磨耗件进行检查与维护。（剩余7946字）